Artificial Intelligence Testing

artificial intelligence testing

Artificial Intelligence testing

Artificial Intelligence testing includes algorithms such as predictive analytics, clustering, time series or sentiment analytics for any business scenario. There are two parts to it :

Testing AI based applications

We have expertise in algorithmic validation of AI/ML and deep learning functionalities which can help test different banking and financial sector applications like Fixed Deposit amount, Recurring Deposit amount, Margins in Security Trading, etc. by creating various test suites. Also automating these test scenarios for making regression testing with ease and grace is an experience we do carry.

Use of AI in testing

Defect analytics

ApMoSys Sentiment Analyzer (ASeA) is Artificial Intelligence based sentiment analytics that categorise the overall sentiment  (positive, negative, neutral) for better decision making.

Real-time dashboard and AI-based predictive analytics: Analytics driven workload modelling for defect prediction, code coverage, response time & scalability prediction.

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Artificial Intelligence test

Challenges in Artificial Intelligence Testing

Testing AI is not like traditional software QA. AI systems evolve with data and often behave unpredictably. Key challenges include lack of test oracles, data bias, continuous learning loops, complex systems and difficulty in reproducing results. At ApMoSys, we tackle these challenges with structured testing strategies and intelligent automation.

Our Tech Stack

We utilise cutting-edge tools and frameworks tailored for AI/ML validation:

  • TensorFlow Extended (TFX) for end-to-end ML pipelines

  • Apache Airflow for orchestration and automation

  • PyTest, Selenium, and custom Python frameworks for unit, integration, and UI testing

  • ASEaP – ApMoSys Sentiment Analyzer for AI-powered sentiment and text classification testing

ApMoSys